arXiv subjects

Allison M. Okamura

Publications and source records attributed to Allison M. Okamura.

At least 55 records · Page 3Linked to original sources

arXiv2023

A Multi-Segment, Soft Growing Robot with Selective Steering

Everting, soft growing vine robots benefit from reduced friction with their environment, which allows them to navigate challenging terrain. Vine robots can use air pouches attached to their sides for lateral steering. However, when all pouches are serially connected, the whole robot can only perform one constant curvature in free space. It must contact the environment to navigate through obstacles along paths with multiple turns. This work presents a multi-segment vine robot that can navigate complex paths without interacting with its environment. This is achieved by a new steering method that selectively actuates each single pouch at the tip, providing high degrees of freedom with few control inputs. A small magnetic valve connects each pouch to a pressure supply line. A motorized tip mount uses an interlocking mechanism and motorized rollers on the outer material of the vine robot. As each valve passes through the tip mount, a permanent magnet inside the tip mount opens the valve so the corresponding pouch is connected to the pressure supply line at the same moment. Novel cylindrical pneumatic artificial muscles (cPAMs) are integrated into the vine robot and inflate to a cylindrical shape for improved bending characteristics compared to other state-of-the-art vine robots. The motorized tip mount controls a continuous eversion speed and enables controlled retraction. A final prototype was able to repeatably grow into different shapes and hold these shapes. We predict the path using a model that assumes a piecewise constant curvature along the outside of the multi-segment vine robot. The proposed multi-segment steering method can be extended to other soft continuum robot designs.

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arXiv2023

Passive Shape Locking for Multi-Bend Growing Inflated Beam Robots

Shape change enables new capabilities for robots. One class of robots capable of dramatic shape change is soft growing "vine" robots. These robots usually feature global actuation methods for bending that limit them to simple, constant-curvature shapes. Achieving more complex "multi-bend" configurations has also been explored but requires choosing the desired configuration ahead of time, exploiting contact with the environment to maintain previous bends, or using pneumatic actuation for shape locking. In this paper, we present a novel design that enables passive, on-demand shape locking. Our design leverages a passive tip mount to apply hook-and-loop fasteners that hold bends without any pneumatic or electrical input. We characterize the robot's kinematics and ability to hold locked bends. We also experimentally evaluate the effect of hook-and-loop fasteners on beam and joint stiffness. Finally, we demonstrate our proof-of-concept prototype in 2D. Our passive shape locking design is a step towards easily reconfigurable robots that are lightweight, low-cost, and low-power.

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arXiv2022

Haptic Feedback Relocation from the Fingertips to the Wrist for Two-Finger Manipulation in Virtual Reality

Relocation of haptic feedback from the fingertips to the wrist has been considered as a way to enable haptic interaction with mixed reality virtual environments while leaving the fingers free for other tasks. We present a pair of wrist-worn tactile haptic devices and a virtual environment to study how various mappings between fingers and tactors affect task performance. The haptic feedback rendered to the wrist reflects the interaction forces occurring between a virtual object and virtual avatars controlled by the index finger and thumb. We performed a user study comparing four different finger-to-tactor haptic feedback mappings and one no-feedback condition as a control. We evaluated users' ability to perform a simple pick-and-place task via the metrics of task completion time, path length of the fingers and virtual cube, and magnitudes of normal and shear forces at the fingertips. We found that multiple mappings were effective, and there was a greater impact when visual cues were limited. We discuss the limitations of our approach and describe next steps toward multi-degree-of-freedom haptic rendering for wrist-worn devices to improve task performance in virtual environments.

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arXiv2022

A Modular 3-Degree-of-Freedom Force Sensor for Robot-assisted Minimally Invasive Surgery Research

Effective force modulation during tissue manipulation is important for ensuring safe robot-assisted minimally invasive surgery (RMIS). Strict requirements for in-vivo distal force sensing have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. These limitations have made collecting high-quality 3-degree-of-freedom (3-DoF) bimanual force data in RMIS inaccessible to researchers. We present a modular and manufacturable 3-DoF force sensor that integrates easily with an existing RMIS tool. We achieve this by relaxing biocompatibility and sterilizability requirements while utilizing commercial load cells and common electromechanical fabrication techniques. The sensor has a range of +-5 N axially and +-3 N laterally with average root mean square errors(RMSEs) of below 0.15 N in all directions. During teleoperated mock tissue manipulation tasks, a pair of jaw-mounted sensors achieved average RMSEs of below 0.15 N in all directions. For grip force, it achieved an RMSE of 0.156 N. The sensor has sufficient accuracy within the range of forces found in delicate manipulation tasks, with potential use in bimanual haptic feedback and robotic force control. As an open-source design, the sensors can be adapted to suit additional robotic applications outside of RMIS.

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arXiv2022

Deep Learning Classification of Touch Gestures Using Distributed Normal and Shear Force

When humans socially interact with another agent (e.g., human, pet, or robot) through touch, they do so by applying varying amounts of force with different directions, locations, contact areas, and durations. While previous work on touch gesture recognition has focused on the spatio-temporal distribution of normal forces, we hypothesize that the addition of shear forces will permit more reliable classification. We present a soft, flexible skin with an array of tri-axial tactile sensors for the arm of a person or robot. We use it to collect data on 13 touch gesture classes through user studies and train a Convolutional Neural Network (CNN) to learn spatio-temporal features from the recorded data. The network achieved a recognition accuracy of 74% with normal and shear data, compared to 66% using only normal force data. Adding distributed shear data improved classification accuracy for 11 out of 13 touch gesture classes.

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arXiv2022

Between-Tactor Display Using Dynamic Tactile Stimuli

Display of illusory vibration locations between physical vibrotactile motors (tactors) placed on the skin has the potential to reduce the number of tactors in distributed tactile displays. This paper presents a between-tactor display method that uses dynamic tactile stimuli to generate illusory vibration locations. A belt with only 6 vibration motors displays 24 targets consisting of on-tactor and between-tactor locations. On-tactor locations are represented by simply vibrating the relevant single tactor. Between-tactor locations are displayed by adjusting the relative vibration amplitudes of two adjacent motors, with either (1) constant vibration amplitudes or (2) perturbed vibration amplitudes (creating local illusory motion). User testing showed that perturbations improve recognition accuracy for in-between tactor localization.

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arXiv2022

Task Dynamics of Prior Training Influence Visual Force Estimation Ability During Teleoperation

The lack of haptic feedback in Robot-assisted Minimally Invasive Surgery (RMIS) is a potential barrier to safe tissue handling during surgery. Bayesian modeling theory suggests that surgeons with experience in open or laparoscopic surgery can develop priors of tissue stiffness that translate to better force estimation abilities during RMIS compared to surgeons with no experience. To test if prior haptic experience leads to improved force estimation ability in teleoperation, 33 participants were assigned to one of three training conditions: manual manipulation, teleoperation with force feedback, or teleoperation without force feedback, and learned to tension a silicone sample to a set of force values. They were then asked to perform the tension task, and a previously unencountered palpation task, to a different set of force values under teleoperation without force feedback. Compared to the teleoperation groups, the manual group had higher force error in the tension task outside the range of forces they had trained on, but showed better speed-accuracy functions in the palpation task at low force levels. This suggests that the dynamics of the training modality affect force estimation ability during teleoperation, with the prior haptic experience accessible if formed under the same dynamics as the task.

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arXiv2022

Toward Force Estimation in Robot-Assisted Surgery using Deep Learning with Vision and Robot State

Knowledge of interaction forces during teleoperated robot-assisted surgery could be used to enable force feedback to human operators and evaluate tissue handling skill. However, direct force sensing at the end-effector is challenging because it requires biocompatible, sterilizable, and cost-effective sensors. Vision-based deep learning using convolutional neural networks is a promising approach for providing useful force estimates, though questions remain about generalization to new scenarios and real-time inference. We present a force estimation neural network that uses RGB images and robot state as inputs. Using a self-collected dataset, we compared the network to variants that included only a single input type, and evaluated how they generalized to new viewpoints, workspace positions, materials, and tools. We found that vision-based networks were sensitive to shifts in viewpoints, while state-only networks were robust to changes in workspace. The network with both state and vision inputs had the highest accuracy for an unseen tool, and was moderately robust to changes in viewpoints. Through feature removal studies, we found that using only position features produced better accuracy than using only force features as input. The network with both state and vision inputs outperformed a physics-based baseline model in accuracy. It showed comparable accuracy but faster computation times than a baseline recurrent neural network, making it better suited for real-time applications.

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arXiv2022

Characterization of Real-time Haptic Feedback from Multimodal Neural Network-based Force Estimates during Teleoperation

Force estimation using neural networks is a promising approach to enable haptic feedback in minimally invasive surgical robots without end-effector force sensors. Various network architectures have been proposed, but none have been tested in real time with surgical-like manipulations. Thus, questions remain about the real-time transparency and stability of force feedback from neural network-based force estimates. We characterize the real-time impedance transparency and stability of force feedback rendered on a da Vinci Research Kit teleoperated surgical robot using neural networks with vision-only, state-only, and state and vision inputs. Networks were trained on an existing dataset of teleoperated manipulations without force feedback. To measure real-time stability and transparency during teleoperation with force feedback to the operator, we modeled a one-degree-of-freedom human and surgeon-side manipulandum that moved the patient-side robot to perform manipulations on silicone artificial tissue over various robot and camera configurations, and tools. We found that the networks using state inputs displayed more transparent impedance than a vision-only network. However, state-based networks displayed large instability when used to provide force feedback during lateral manipulation of the silicone. In contrast, the vision-only network showed consistent stability in all the evaluated directions. We confirmed the performance of the vision-only network for real-time force feedback in a demonstration with a human teleoperator.

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arXiv2022

Wearable Haptic Device for Individuals with Congenital Absence of Proprioception

A rare genetic condition, PIEZO2 loss of function (LOF) is characterized by absence of proprioception and light touch, which makes functional tasks (e.g., walking, manipulation) difficult. There are no pharmacological treatments or assistive technologies available for individuals with PIEZO2-LOF. We propose a sensory substitution device that communicates proprioceptive feedback via detectable haptic stimuli. We created a wearable prototype that maps measurements of elbow movement to deep pressure applied to the forearm. The prototype applies up to 18 N, includes an embedded force sensor, and is programmable to allow for various angle-to-pressure mappings. Future work includes comparing proprioceptive acuity and movement ability with and without the device in healthy and PIEZO2-LOF individuals, developing low-profile devices using soft robotics, providing sensory substitution for multiple joints simultaneously, and encoding additional aspects of joint dynamics.

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arXiv2022

Feasibility of Smartphone Vibrations as a Sensory Diagnostic Tool

Traditionally, clinicians use tuning forks as a binary measure to assess vibrotactile sensory perception. This approach has low measurement resolution, and the vibrations are highly variable. Therefore, we propose using vibrations from a smartphone to deliver a consistent and precise sensory test. First, we demonstrate that a smartphone has more consistent vibrations compared to a tuning fork. Then we develop an app and conduct a validation study to show that the smartphone can precisely measure a user's absolute threshold. This finding motivates future work to use smartphones to assess vibrotactile perception, allowing for increased monitoring and widespread accessibility.

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arXiv2022

Perception of Mechanical Properties via Wrist Haptics: Effects of Feedback Congruence

Despite non-co-location, haptic stimulation at the wrist can potentially provide feedback regarding interactions at the fingertips without encumbering the user's hand. Here we investigate how two types of skin deformation at the wrist (normal and shear) relate to the perception of the mechanical properties of virtual objects. We hypothesized that a congruent mapping between force at the fingertips and deformation at the wrist would be better, i.e. mapping finger normal force to skin indentation at the wrist, and shear force to skin shear at the wrist, would result in better perception than other mappings that either mixed or merged the two direction into a single type of feedback. We performed an experiment where haptic devices at the wrist rendered either normal or shear feedback during manipulation of virtual objects with varying stiffness, mass, or friction properties. Perception of mechanical properties was more accurate with congruent skin stimulation than noncongruent. In addition, discrimination performance and subjective reports were positively influenced by congruence. This study demonstrates that users can perceive mechanical properties via haptic feedback provided at the wrist with a consistent mapping between haptic feedback and interaction forces at the fingertips, regardless of congruence.

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arXiv2022

Effects of Haptic Feedback on the Wrist during Virtual Manipulation

We propose a haptic system for virtual manipulation to provide feedback on the user's forearm instead of the fingertips. In addition to visual rendering of the manipulation with virtual fingertips, we employ a device to deliver normal or shear skin-stretch at multiple points near the wrist. To understand how design parameters influence the experience, we investigated the effect of the number and location of sensory feedback on stiffness perception. Participants compared stiffness values of virtual objects, while the haptic bracelet provided interaction feedback on the dorsal, ventral, or both sides of the wrist. Stiffness discrimination judgments and duration, as well as qualitative results collected verbally, indicate no significant difference in stiffness perception with stimulation at different and multiple locations.

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arXiv2022

Perceived Intensities of Normal and Shear Skin Stimuli using a Wearable Haptic Bracelet

Our aim is to provide effective interaction with virtual objects, despite the lack of co-location of virtual and real-world contacts, while taking advantage of relatively large skin area and ease of mounting on the forearm. We performed two human participant studies to determine the effects of haptic feedback in the normal and shear directions during virtual manipulation using haptic devices worn near the wrist. In the first study, participants performed significantly better while discriminating stiffness values of virtual objects when the feedback consisted of normal displacements compared to shear displacements. Participants also commented that they could detect normal cues much easier than shear, which motivated us to perform a second study to find the point of subjective equality (PSE) between normal and shear stimuli. Our results show that shear stimuli require a larger actuator displacement but less force than normal stimuli to achieve perceptual equality for our haptic bracelets. We found that normal and shear stimuli cannot be equalized through skin displacement nor the interaction forces across all users. Rather, a calibration method is needed to find the point of equality for each user where normal and shear stimuli create the same intensity on the user's skin.

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arXiv2022

FingerPrint: A 3-D Printed Soft Monolithic 4-Degree-of-Freedom Fingertip Haptic Device with Embedded Actuation

Wearable fingertip haptic interfaces provide tactile stimuli on the fingerpads by applying skin pressure, linear and rotational shear, and vibration. Designing and fabricating a compact, multi-degree-of-freedom, and forceful fingertip haptic interface is challenging due to trade-offs among miniaturization, multifunctionality, and manufacturability. Downsizing electromagnetic actuators that produce high torques is infeasible, and integrating multiple actuators, links, joints, and transmission elements increases device size and weight. 3-D printing enables rapid manufacturing of complex devices with minimal assembly in large batches. However, it requires a careful arrangement of material properties, geometry, scale, and printer capabilities. Here we present a fully 3-D printed, soft, monolithic fingertip haptic device based on an origami pattern known as the "waterbomb" base that embeds foldable vacuum actuation and produces 4-DoF of motion on the fingerpad with tunable haptic forces (up to 1.3 N shear and 7 N normal) and torque (up to 25 N-mm). Including the thimble mounting, the compact device is 40 mm long and 20 mm wide. This demonstrates the efficacy of origami design and soft material 3D printing for designing and rapidly fabricating miniature yet complex wearable mechanisms with force output appropriate for haptic interaction.

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arXiv2022

Social-Cultural Factors in the Design of Technology for Hispanic People with Stroke

Stroke is a leading cause of serious, long-term disability in the United States. There exist disparities in both stroke prevalence and outcomes between people with stroke in Hispanic and Latinx communities and the general stroke population. Current stroke technology - which aims to improve quality of life and bring people with stroke to the most functional, independent state possible - has shown promising results for the general stroke population, but has failed to close the recovery outcome gap for underserved Hispanic and Latinx people with stroke. Previous work in health education, digital health, and HRI has improved human health outcomes by incorporating social-cultural factors, though not for stroke. In this position paper, we aim to justify accounting for unique cultural factors in stroke technology design for the Hispanic and Latinx community. We review examples of successful culturally appropriate interventions and suggest design considerations (mutually beneficial community consultation, accommodating for barriers beforehand, building on culture, and incorporating education of the family) to provide more culturally appropriate design of Hispanic and Latinx stroke technology and reduce the disparity gap.

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arXiv2021

Data-driven sparse skin stimulation can convey social touch information to humans

During social interactions, people use auditory, visual, and haptic cues to convey their thoughts, emotions, and intentions. Due to weight, energy, and other hardware constraints, it is difficult to create devices that completely capture the complexity of human touch. Here we explore whether a sparse representation of human touch is sufficient to convey social touch signals. To test this we collected a dataset of social touch interactions using a soft wearable pressure sensor array, developed an algorithm to map recorded data to an array of actuators, then applied our algorithm to create signals that drive an array of normal indentation actuators placed on the arm. Using this wearable, low-resolution, low-force device, we find that users are able to distinguish the intended social meaning, and compare performance to results based on direct human touch. As online communication becomes more prevalent, such systems to convey haptic signals could allow for improved distant socializing and empathetic remote human-human interaction.

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arXiv2021

Geometric Solutions for General Actuator Routing on Inflated-Beam Soft Growing Robots

Continuum and soft robots can leverage complex actuator shapes to take on useful shapes while actuating only a few of their many degrees of freedom. Continuum robots that also grow increase the range of potential shapes that can be actuated and enable easier access to constrained environments. Existing models for describing the complex kinematics involved in general actuation of continuum robots rely on simulation or well-behaved stress-strain relationships, but the non-linear behavior of the thin-walled inflated-beams used in growing robots makes these techniques difficult to apply. Here we derive kinematic models of single, generally routed tendon paths on a soft pneumatic backbone of inextensible but flexible material from geometric relationships alone. This allows for forward modeling of the resulting shapes with only knowledge of the geometry of the system. We show that this model can accurately predict the shape of the whole robot body and how the model changes with actuation type. We also demonstrate the use of this kinematic model for inverse design, where actuator designs are found based on desired final robot shapes. We deploy these designed actuators on soft pneumatic growing robots to show the benefits of simultaneous growth and shape change.

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